CompactRegressionGAM

Compact generalized additive model (GAM) for regression

Since R2021a

Description

CompactRegressionGAM is a compact version of a RegressionGAM model object (GAM for regression). The compact model does not include the data used for training the model. Therefore, you cannot perform some tasks, such as cross-validation, using the compact model. Use a compact model for tasks such as predicting the responses of new data.

Creation

Create a CompactRegressionGAM object from a full RegressionGAM model object by using compact.

Properties

GAM Properties

Interactions — Interaction term indices

two-column matrix of positive integers | []

This property is read-only.

Interaction term indices, specified as a t-by-2 matrix of positive integers, where t is the number of interaction terms in the model. Each row of the matrix represents one interaction term and contains the column indexes of the predictor data X for the interaction term. If the model does not include an interaction term, then this property is empty ([]).

The software adds interaction terms to the model in the order of importance based on the p-values. Use this property to check the order of the interaction terms added to the model.

Data Types: double

Intercept — Intercept term of model

numeric scalar

This property is read-only.

Intercept (constant) term of the model, which is the sum of the intercept terms in the predictor trees and interaction trees, specified as a numeric scalar.

Data Types: single | double

IsStandardDeviationFit — Flag indicating whether standard deviation model is fit

false | true

Flag indicating whether a model for the standard deviation of the response variable is fit, specified as false or true. Specify the 'FitStandardDeviation' name-value argument of fitrgam as true to fit the model for the standard deviation.

If IsStandardDeviationFit is true, then you can evaluate the standard deviation at a new observation by using predict. This function also returns the prediction intervals of the response variable, evaluated at given observations.

Data Types: logical

Other Regression Properties

CategoricalPredictors — Categorical predictor indices

vector of positive integers | []

This property is read-only.

Categorical predictor indices, specified as a vector of positive integers. CategoricalPredictors contains index values indicating that the corresponding predictors are categorical. The index values are between 1 and p, where p is the number of predictors used to train the model. If none of the predictors are categorical, then this property is empty ([]).

Data Types: double

ExpandedPredictorNames — Expanded predictor names

cell array of character vectors

This property is read-only.

Expanded predictor names, specified as a cell array of character vectors.

ExpandedPredictorNames is the same as PredictorNames for a generalized additive model.

Data Types: cell

PredictorNames — Predictor variable names

cell array of character vectors

This property is read-only.

Predictor variable names, specified as a cell array of character vectors. The order of the elements in PredictorNames corresponds to the order in which the predictor names appear in the training data.

Data Types: cell

ResponseName — Response variable name

character vector

This property is read-only.

Response variable name, specified as a character vector.

Data Types: char

ResponseTransform — Response transformation function

'none' | function handle

Response transformation function, specified as 'none' or a function handle. ResponseTransform describes how the software transforms raw response values.

For a MATLAB^® function or a function that you define, enter its function handle. For example, you can enter Mdl.ResponseTransform = @function, where function accepts a numeric vector of the original responses and returns a numeric vector of the same size containing the transformed responses.

Data Types: char | function_handle

Object Functions

Interpret Prediction

lime	Local interpretable model-agnostic explanations (LIME)
partialDependence	Compute partial dependence
plotLocalEffects	Plot local effects of terms in generalized additive model (GAM)
plotPartialDependence	Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots
shapley	Shapley values

Assess Predictive Performance on New Observations

predict	Predict responses using generalized additive model (GAM)
loss	Regression loss for generalized additive model (GAM)

Examples

Reduce Size of Generalized Additive Model

Reduce the size of a full generalized additive model (GAM) for regression by removing the training data. Full models hold the training data. You can use a compact model to improve memory efficiency.

Load the carbig data set.

load carbig

Specify Acceleration, Displacement, Horsepower, and Weight as the predictor variables (X) and MPG as the response variable (Y).

X = [Acceleration,Displacement,Horsepower,Weight];
Y = MPG;

Train a GAM using X and Y.

Mdl = fitrgam(X,Y)

Mdl = 
  RegressionGAM
              ResponseName: 'Y'
     CategoricalPredictors: []
         ResponseTransform: 'none'
                 Intercept: 26.9442
    IsStandardDeviationFit: 0
           NumObservations: 398

Mdl is a RegressionGAM model object.

Reduce the size of the model.

CMdl = compact(Mdl)

CMdl = 
  CompactRegressionGAM
              ResponseName: 'Y'
     CategoricalPredictors: []
         ResponseTransform: 'none'
                 Intercept: 26.9442
    IsStandardDeviationFit: 0

CMdl is a CompactRegressionGAM model object.

Display the amount of memory used by each regression model.

whos('Mdl','CMdl')

  Name      Size             Bytes  Class                                          Attributes

  CMdl      1x1             597222  classreg.learning.regr.CompactRegressionGAM              
  Mdl       1x1             631046  RegressionGAM                                            

The full model (Mdl) is larger than the compact model (CMdl).

To efficiently predict responses for new observations, you can remove Mdl from the MATLAB® Workspace, and then pass CMdl and new predictor values to predict.

Version History

Introduced in R2021a